Construction method of high and large formwork engineering supporting system

Abstract

The invention discloses a high and large formwork engineering supporting system construction method, and belongs to the technical field of building engineering. Comprising the following steps: step 1, arranging intelligent nodes in a high and large template support system to realize information sharing and group cooperative regulation and control among the nodes; step 2, dividing the support system into a plurality of mutually associated load control areas, and setting differentiated load control parameters; step 3, automatically optimizing a pouring path; 4, when the concrete strength reaches a preset threshold value, the supporting rigidity or the reserved deformation amount is automatically adjusted according to a preset program, and the concrete structure bears the dead weight step by step; 5, the temporary load is automatically compensated, when the load exceeds a safety threshold value, a load redistribution mechanism and a reinforcing device are triggered, meanwhile, an alarm is started, and abnormal data are pushed; and step 6, continuously optimizing control parameters, accumulating construction data, constructing a digital construction record and a knowledge base, and realizing intelligent iterative upgrading of the construction method.

Description

Technical Field

[0001] This application relates to the technical field of construction engineering, and more specifically, to a construction method for the support system of high and large formwork projects. Background Art

[0002] The support system of high and large formwork projects is widely used in fields such as concrete pouring and structural support in construction engineering. Especially in projects such as high-rise buildings, bridges, and tunnels, it plays a key role in support and fixation. Traditional high and large formwork support systems mostly use components such as steel pipe supports, wooden formwork, or steel formwork. Although they have a certain support capacity, there are some obvious deficiencies in the actual construction process. For example, the installation and removal operations of the support system during construction are relatively cumbersome, the stability of the support structure is difficult to guarantee, the connection between the formwork and the support system is not tight enough, and problems such as slippage and dislocation are likely to occur. This not only affects the construction progress but also may pose potential safety hazards to the project.

[0003] With the continuous expansion of the scale of construction projects, the complexity of the construction environment is also increasing. Traditional support systems are gradually showing a series of limitations when facing high-strength and high-precision construction requirements. Especially in the case of narrow construction sites, dense personnel, high and complex working surfaces, the construction efficiency and safety of traditional support systems are difficult to meet the requirements of modern construction projects for construction quality and construction speed. In addition, the existing support systems also face problems such as large weight, inconvenient transportation and storage, resulting in an increase in project costs.

[0004] In order to overcome the deficiencies of traditional support systems, many new support technologies have emerged. Technologies such as intelligent adjustment, modular combination, and lightweight materials have been gradually applied to the support system of high and large formwork projects. These new support systems have certain advantages in improving construction efficiency, ensuring support stability, and enhancing safety. However, the existing high and large formwork support systems still face problems such as how to optimize support design, improve the level of construction automation, and enhance the flexibility of the system. Especially when ensuring high-strength support, it is also necessary to consider the simplicity and operability of the system.

[0005] In summary, how to ensure the safety of the high and large formwork support system while improving its construction efficiency, reducing the operation difficulty, and lowering the cost has become a technical problem that urgently needs to be solved. Summary of the Invention

[0006] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a construction method for the support system of high and large formwork projects in view of the above problems, which is characterized by including the following steps:

[0007] Step 1: Arrange intelligent nodes with load perception and automatic adjustment functions in the high formwork support system, construct a hierarchical control network, and achieve information sharing and group collaborative regulation among nodes;

[0008] Step 2: Divide the support system into multiple interrelated load control areas, set differentiated load control parameters, and optimize the concrete pouring rate, support adjustment strategy, and monitoring frequency;

[0009] Step 3: Dynamically adjust the concrete pouring rate, sequence, and position according to the real-time monitoring data, and automatically optimize the pouring path;

[0010] Step 4: When the concrete strength reaches the preset threshold, automatically adjust the support stiffness or reserved deformation amount according to the preset program, so that the concrete structure gradually bears its own weight;

[0011] Step 5: Automatically compensate for the temporary load. When the load exceeds the safety threshold, trigger the load redistribution mechanism and reinforcement device, and at the same time start the alarm and push the abnormal data;

[0012] Step 6: Continuously optimize the control parameters, accumulate construction data, construct a digital construction record and knowledge base, and realize the intelligent iterative upgrade of the construction method.

[0013] Further, Step 1 includes the following steps:

[0014] According to the structural characteristics and force analysis of the support system, determine the positions of key control points, and install intelligent nodes at the intersection points of vertical poles, the connection points of horizontal bars, and the load concentration areas;

[0015] Configure a micro-displacement actuator for each intelligent node to achieve active deformation control of the support structure;

[0016] Integrate a wireless data transmission module into each intelligent node;

[0017] Establish a three-level control network architecture, where the on-site control layer consists of intelligent nodes distributed in various regions of the support system, the regional control layer is responsible for coordinating the operation of nodes in the same load area, and the central control layer coordinates the overall operation strategy and provides data analysis and decision-making support;

[0018] When an individual node detects an abnormal load change, adjacent nodes will automatically adjust the support stiffness according to the preset logic, thus realizing a distributed adaptive regulation mechanism;

[0019] Verify the measurement accuracy of each node by applying known loads, and simulate various working conditions to test the collaborative response ability between nodes to ensure that the entire control network is in the best working state before formal construction.

[0020] Furthermore, each intelligent node is equipped with a high-precision pressure sensor with a measurement accuracy of ±0.01 kN, a range of 0 - 150 kN, and an adjustable sampling frequency within the range of 0.2 - 20 Hz; the micro-displacement actuator adopts an electro-hydraulic hybrid drive method with an execution accuracy of 0.01 mm, an adjustment stroke of ±15 mm, and a response time of less than 0.5 s; the intelligent node is built-in with a 64-bit microprocessor and 4 GB of flash memory, capable of storing 72 hours of high-frequency sampling data and completing Fourier transform and wavelet analysis locally; the wireless data transmission module uses multi-band communication technology, with operating frequency bands covering 433 MHz, 868 MHz, and 2.4 GHz, a data transmission rate of 5 Mbps, and an anti-interference ability of not less than 85 dB; the intelligent node adopts a tiered power supply system, including mains power, backup batteries, and energy harvesting units, ensuring that the core functions can continue to work for 96 hours in the event of a complete power outage.

[0021] Furthermore, Step 2 includes the following steps:

[0022] Establish a BIM model of the support system and import it into the finite element analysis software to construct a mechanical model that comprehensively considers contact nonlinearity, material nonlinearity, and large geometric deformations. At the same time, through multi-condition load analysis, identify stress concentration areas, deformation-sensitive areas, and potential instability positions;

[0023] Based on the finite element analysis results, divide the entire support system into several load control areas, and configure independent control parameters and monitoring schemes for each control area, while ensuring coordinated transition between adjacent areas and the integrity of the load transfer path;

[0024] Set differentiated control parameters for different load control areas;

[0025] Based on the rheological mechanics principle and the early strength development law of concrete, optimize the pouring process parameters, determine the optimal pouring rate curve, and formulate an accurate pouring sequence for different layers and areas;

[0026] Set differentiated monitoring frequencies for different construction stages. Specifically: high-frequency monitoring is adopted in the initial pouring stage, reduced to medium-frequency monitoring during the concrete setting period, and low-frequency monitoring is used during the curing period; at the same time, establish an automatic trigger mechanism based on load mutations to ensure that abnormal situations can be identified and responded to in a timely manner;

[0027] Develop multi-condition emergency plans for possible construction abnormal situations.

[0028] Furthermore, Step 3 includes the following steps:

[0029] The load data monitored in real time by the intelligent node is compared and analyzed with the preset safety threshold. When the load growth rate exceeds 0.5kN / min or the load difference between adjacent nodes is greater than 15%, the pouring parameter adjustment instruction is automatically generated and executed to achieve precise control of the pouring rate, tank truck layout and delivery pump working intensity.

[0030] Based on the load distribution cloud map, the pouring path is calculated and optimized in real time to ensure that the concrete pouring sequence meets the force requirements and avoid local overload;

[0031] Real-time calculation of the best pouring strategy, optimizing the vibration density and surface flatness of concrete while ensuring that the stress level of the support system does not exceed 75% of the design value, to achieve coordinated control of safety and quality;

[0032] When it is detected that the load in a local area increases too fast or the deformation exceeds expectations, the three-level response is automatically triggered, among which: the first-level response adjusts the pouring rate to 70% of the normal value; the second-level response suspends the pouring in the area and transfers it to the low-load area; the third-level response starts the emergency unloading procedure to ensure that the abnormal load is controlled in a timely and effective manner;

[0033] The load level and change trend of each area are displayed in real time through color gradients. At the same time, a virtual pouring simulation module is established to conduct digital previews before actual pouring, simulate the structural response under different pouring schemes, and optimize the construction plan;

[0034] When an abnormal increase in load is detected in a local area, nearby intelligent nodes work together to evenly distribute the load to the surrounding supporting structures by slightly lifting or unloading to adjust the force transmission path, ensuring that the structural deformation remains continuous and controllable.

[0035] Further, step 4 includes the following steps:

[0036] By measuring the changes in temperature and sound velocity inside the concrete, its real-time strength can be accurately calculated. When the strength reaches 30%, 60% and 75% of the design value, the corresponding support adjustment program is automatically triggered;

[0037] A graded unloading strategy is adopted, and the unloading process is divided into three stages: preparatory adjustment period, gradual unloading period and stable monitoring period. A progressive control strategy of "trial adjustment-observation-re-adjustment" is implemented. When the concrete strength reaches 30% of the design value, a micro-adjustment of 1-3mm is first made to the edge support of the non-critical path. After observing the structural response, it is then orderly advanced to the critical area to ensure that the unloading process is controllable and reversible.

[0038] Dynamically adjust the unloading speed according to the structural deformation rate to ensure that the deformation of key positions is controlled within the structural span / 2000, preventing the concrete from cracking or excessive deflection in the process of gradually bearing its own weight;

[0039] Through data analysis of intelligent nodes, the load transfer curve between the support system and the concrete structure is plotted in real time. When the load transfer rate is detected to be abnormal or the local stress suddenly changes, the unloading program is automatically paused and the current state is maintained until the structural stress distribution is stable again;

[0040] For long-span or high-difficulty structures, a differential support adjustment strategy is implemented. The stiffness adjustment method is adopted in the core stress area, and the concrete structure gradually bears the load by reducing the support stiffness; the reserved deformation method is adopted in the edge and secondary areas to ensure the structural stress balance and deformation coordination;

[0041] Record the unloading time, adjustment amount and structural response data, generate the unloading progress report and safety assessment in real time, and at the same time associate with the BIM model to form a visual simulation of the unloading process.

[0042] Furthermore, step 5 includes the following steps:

[0043] When local load fluctuations are detected, the built-in buffer device of the intelligent node automatically absorbs the stress peak, preventing the impact load from being directly transmitted to the overall support structure, and at the same time recording the deformation amount and recovery time;

[0044] When the load reaches 85% of the design value, it enters the yellow warning state and the monitoring frequency is automatically increased; when it reaches 95% of the design value, the orange warning is started and the adjacent nodes are triggered to share the load jointly; when it exceeds the design value, the red warning is immediately executed, and the active unloading program and the structural reinforcement strategy are started;

[0045] Based on the topology optimization algorithm, the optimal load transfer path is dynamically calculated. By fine-tuning the support stiffness of adjacent nodes, an elastic support network with "soft-hard" alternation is formed, effectively flattening the stress gradient in the overloaded area and evenly dispersing the load to the surrounding low-load areas to ensure the balanced stress of the overall structure;

[0046] When the structural deformation trend is detected to intensify, the optimal reinforcement position and support angle are automatically calculated, and the support deployment is quickly completed through remote control or a nearby robotic arm to improve the bearing capacity of the local area;

[0047] Automatically generate event tags, classify them according to the severity level, and at the same time provide a preliminary diagnosis of the cause of the failure and disposal suggestions to guide the on-site personnel to implement precise intervention and improve the response efficiency;

[0048] By comparing the historical load fluctuation patterns and intervention effects, the warning thresholds and response strategies are continuously optimized.

[0049] Furthermore, the intelligent node buffer device has the following parameters:

[0050] The buffer device adopts a gas-liquid composite structure, the buffer stroke is 8-20mm, and the maximum absorption capacity is 35% of the design load;

[0051] The buffer response time is less than 0.15 seconds, and the maximum deceleration is controlled within 5g, effectively preventing structural damage caused by impact loads;

[0052] The buffer device is equipped with a displacement sensor and an accelerometer, with a sampling frequency of 200Hz, accurately recording the load-time curve during the impact process;

[0053] The device has a three-stage buffer mechanism: mild impacts are absorbed through elastic deformation; moderate impacts are absorbed through controllable damping; severe impacts are absorbed through a controllable hydraulic system;

[0054] The buffer device has a self-recovery function, recovering 90% of its function within 8 - 15 seconds after mild and moderate impacts, and 80% of its function within 30 - 60 seconds after severe impacts.

[0055] Furthermore, based on the topology optimization algorithm, dynamically calculate the optimal load transfer path, and form an elastic support network with "soft-hard" alternation by fine-tuning the support stiffness of adjacent nodes, effectively flattening the stress gradient in the overload area and evenly dispersing the load to the surrounding low-load areas to ensure the overall structural stress balance, including the following steps:

[0056] Construct an initial finite element model and set the load and constraint conditions, establish a discrete grid in the simulation domain and define the initial density and stiffness properties of each node, and at the same time establish the optimization objective function and related constraint conditions;

[0057] Execute the topology optimization iterative calculation, identify the key load transfer paths through sensitivity analysis, dynamically update the density values of each grid element according to the stress distribution, retain the main load-bearing paths and gradually eliminate the areas with inefficient material distribution until the load transfer efficiency reaches the optimum;

[0058] Based on the optimization results, construct a "soft-hard" alternating layout plan, arrange high-stiffness support nodes around the high-stress area, and configure relatively flexible support structures in the adjacent areas to form a stiffness gradient distribution to ensure that the load can be smoothly transferred along the preset path;

[0059] Adjust the stiffness coefficients of each node through parametric design to make the stiffness distribution form a complementary relationship with the stress distribution, establish an elastic support network that can adaptively deform, and improve the adaptability of the structure to local overloads;

[0060] Compare the stress concentration coefficient and peak stress level before and after optimization to confirm whether the stress gradient in the overload area is effectively flattened and whether the load is successfully dispersed to the surrounding low-load areas;

[0061] Analyze the performance of the optimized structure under different load conditions and boundary conditions to ensure that it can maintain stress balance in various operating environments.

[0062] Further, step 6 includes the following steps:

[0063] Adopt a hierarchical and progressive data acquisition strategy to capture load changes, displacement responses, and environmental parameters in real time, and synchronously record the construction progress and operation instructions. Screen key data through edge computing and upload it to the cloud to construct a multi-dimensional construction holographic record with spatio-temporal tagging;

[0064] Adopt the Bayesian optimization method to dynamically adjust the load threshold, response sensitivity, and control gain. Automatically generate an optimal control parameter set by comparing the stability and energy consumption efficiency under different parameter combinations to ensure that the support system accurately adapts to different structural types and construction conditions;

[0065] Refine and standardize the construction experience and abnormal handling strategies in individual projects, and establish a retrievable construction knowledge graph through semantic annotation and association mapping to improve the knowledge reuse rate and the intelligent level of project management;

[0066] Compare the actual construction process with the virtual simulation model in real time, quantify the prediction deviation and the actual state difference, and continuously optimize based on the closed-loop feedback mechanism to ensure construction accuracy and structural safety;

[0067] By comprehensively analyzing the spatio-temporal distribution law and correlation factors of historical abnormal data, accurately identify potential risk points and weak links, deploy monitoring resources and prevention and control measures in advance, and transform passive emergency into active prevention, significantly reducing the probability of construction risks.

[0068] Compared with the prior art, the present application has the following beneficial effects:

[0069] The present application realizes the dynamic adaptive control of the high formwork support system by arranging intelligent nodes with load sensing and automatic adjustment functions, optimizes the support adjustment, load distribution, and risk warning during concrete pouring, improves construction safety and accuracy, and at the same time combines the BIM model and the topology optimization algorithm to realize the intelligent design of the support system and the iterative upgrade of the construction method. Description of the Drawings

[0070] Figure 1 It is a schematic flow chart of a construction method for a high formwork engineering support system disclosed in an embodiment of the present application. Detailed Embodiments

[0071] To make the purpose, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The described embodiments are some, but not all, of the embodiments of the present invention.

[0072] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0073] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary and are intended to explain the present invention, and should not be construed as a limitation to the present invention.

[0074] As Figure 1 shown, a construction method for a high formwork engineering support system includes the following steps:

[0075] Step 1, arrange intelligent nodes with load sensing and automatic adjustment functions in the high formwork support system, construct a hierarchical control network, and realize information sharing and group collaborative control among the nodes;

[0076] Step 2, divide the support system into multiple interrelated load control areas, set differentiated load control parameters, optimize the concrete pouring rate, support adjustment strategy and monitoring frequency, and formulate emergency plans under multiple working conditions at the same time;

[0077] Step 3, dynamically adjust the concrete pouring rate, sequence and position according to the real-time monitoring data, automatically optimize the pouring path, ensure the balanced force of the support nodes, and timely adjust abnormal loads to prevent structural instability;

[0078] Step 4, when the concrete strength reaches the preset threshold, automatically adjust the support stiffness or reserved deformation amount according to the preset program, so that the concrete structure gradually bears its own weight;

[0079] Step 5, automatically compensate for temporary loads. When the load exceeds the safety threshold, trigger the load redistribution mechanism and reinforcement device, and at the same time start the alarm and push abnormal data to ensure timely intervention;

[0080] Step 6, continuously optimize the control parameters, accumulate construction data, construct a digital construction record and knowledge base, and realize the intelligent iterative upgrade of the construction method.

[0081] In this embodiment, Step 1 enables the support system to sense the load conditions of each node in real time and automatically adjust according to the actual situation. During the actual construction process, through the sensing function of the intelligent nodes, abnormal changes in the load can be detected in a timely manner. For example, if the load in a certain area suddenly increases, this information can be quickly transmitted to other relevant nodes, triggering the automatic adjustment mechanism, thereby avoiding problems such as structural instability caused by excessive local load. At the same time, the construction of the hierarchical control network makes the entire support system form an organic whole, and the nodes can cooperate and coordinate with each other, improving the overall stability and reliability, and effectively reducing the construction risk.

[0082] In this embodiment, step 2 can perform precise control according to the characteristics and load conditions of different regions, improving the scientificity and rationality of construction. During actual construction, due to differences in factors such as function and structural form in different regions, the load conditions will also vary. By dividing the load control areas, the most suitable construction parameters and control strategies can be formulated for each area, avoiding problems that may be caused by a "one-size-fits-all" construction method. At the same time, optimizing parameters such as the concrete pouring rate can ensure the pouring quality of concrete, preventing structural defects or construction delays caused by too fast or too slow pouring. The emergency plans under multiple working conditions provide countermeasures for various unexpected situations that may occur during the construction process, enhancing the controllability and safety of construction. Once an abnormal situation occurs, the corresponding emergency plan can be quickly activated to minimize losses and impacts.

[0083] In this embodiment, step 3 makes full use of modern monitoring technologies and automated control means to achieve refined management of the construction process. During the construction process, through the feedback of real-time monitoring data, it is possible to timely understand the impact of concrete pouring on the support system. For example, if the force on a certain support node increases due to concrete pouring, the pouring parameters can be automatically adjusted and the pouring path optimized to make the load reasonably distributed in the support system. This dynamic adjustment mechanism can not only ensure the balanced force on the support nodes, avoiding structural damage caused by excessive local force, but also improve construction efficiency, reduce construction delays and resource waste caused by unreasonable pouring, and ensure the smooth progress of the construction process.

[0084] In this embodiment, step 4 fully considers the development law of concrete strength and the gradual process of structural force, realizing the coordinated work between the support system and the concrete structure. After the concrete is poured, its strength will gradually increase with time. During this process, the support system needs to gradually transfer the load to the concrete structure so that it can gradually bear its own weight. By automatically adjusting the support stiffness or reserving the deformation amount, when the concrete strength reaches a certain requirement, the load between the support system and the concrete structure can be reasonably distributed, avoiding uneven force or damage to the concrete structure caused by premature removal of the support system, and also preventing potential safety hazards caused by the support system bearing excessive load for a long time. This way of gradually transferring the load conforms to the mechanical property development characteristics of the concrete structure and is beneficial to improving the safety of the entire construction process and the final quality of the structure.

[0085] In this embodiment, step 5 can timely respond to the changes in these temporary loads through an automatic compensation and redistribution mechanism. When the temporary load exceeds the safety range, the load redistribution will be automatically triggered, transferring part of the load to other relatively safe areas, and the reinforcement device will be activated to enhance the bearing capacity of the support system. At the same time, the activation of the alarm and the push of abnormal data can timely notify the construction personnel to take corresponding intervention measures, further ensuring the construction safety and avoiding structural instability or collapse accidents caused by excessive temporary loads.

[0086] In this embodiment, step 6 reflects the intelligent development trend of modern construction technology. By continuously collecting and analyzing various data during the construction process, the control parameters are optimized and adjusted, enabling the construction method to continuously adapt to new construction conditions and requirements. In actual construction, a large amount of data is generated during each construction. These data contain various information during the construction process, such as load changes, concrete strength development, and the stress conditions of the support system. By mining and analyzing these data, more optimal construction parameters and control strategies can be summarized, providing reference for subsequent construction. At the same time, a digital construction record and knowledge base are constructed to save and inherit these valuable experiences and data, realizing the intelligent iterative upgrade of the construction method, improving the technical level and construction ability of the entire construction team, and providing more reliable technical support for the construction of the high formwork engineering support system in the future.

[0087] To sum up, the construction method of the high formwork engineering support system realizes the refined, intelligent, and safe management of the construction process through the coordinated action of multiple steps such as intelligent nodes and hierarchical control networks, load control area division and multi-condition emergency management, real-time monitoring data and dynamic adjustment, concrete strength and support stiffness adjustment, temporary load compensation and redistribution mechanisms, and control parameter optimization and intelligent iterative upgrade. It can effectively respond to various load changes and abnormal situations that may occur during the construction process, improving the construction quality and efficiency.

[0088] Furthermore, step 1 includes the following steps:

[0089] According to the structural characteristics and force analysis of the support system, determine the positions of key control points, and install intelligent nodes at the intersection points of vertical poles, the connection points of horizontal bars, and the load concentration areas to achieve comprehensive monitoring of the stress state of the support system;

[0090] Configure a micro-displacement actuator for each intelligent node to ensure automatic fine-tuning when abnormal load distribution is detected, realizing the active deformation control of the support structure;

[0091] Integrate a wireless data transmission module into each intelligent node to ensure the high efficiency and response ability of real-time data transmission;

[0092] Establish a three - level control network architecture, where the on - site control layer consists of intelligent nodes distributed in various regions of the support system. The regional control layer is responsible for coordinating the operation of nodes within the same load area, and the central control layer coordinates the overall operation strategy and provides data analysis and decision - making support;

[0093] Develop a group collaborative regulation algorithm to enable adjacent intelligent nodes to sense each other's states and respond collaboratively. Specifically: when a single node detects an abnormal load change, adjacent nodes will automatically adjust the support stiffness according to a preset logic, thereby implementing a distributed adaptive regulation mechanism;

[0094] Verify the measurement accuracy of each node by applying known loads, and simulate various working conditions to test the collaborative response ability between nodes, ensuring that the entire control network is in the best working state before formal construction.

[0095] In summary, by installing intelligent nodes at key positions in the support system, configuring micro - displacement actuators and wireless data transmission modules, constructing a three - level control network architecture, and developing a group collaborative regulation algorithm, comprehensive monitoring of the stress state of the support system and active deformation control have been achieved. The intelligent nodes can real - time sense load changes and achieve adaptive adjustment of the support structure through fine - tuning to ensure balanced stress. The wireless data transmission module ensures efficient data transmission and rapid response, while the three - level control network architecture realizes collaborative regulation from local to overall, improving stability and reliability. The group collaborative regulation algorithm enables adjacent nodes to sense and respond to each other, further enhancing the adaptive ability. Through load verification and working condition simulation tests, the high precision and high reliability of the control network before formal construction are ensured, effectively reducing construction risks, improving construction quality and efficiency, and providing an intelligent and refined solution for the construction of the support system of high - formwork projects.

[0096] Furthermore, each intelligent node is equipped with a high - precision pressure sensor with a measurement accuracy of ±0.01 kN, a range of 0 - 150 kN, and the sampling frequency can be adaptively adjusted within the range of 0.2 - 20 Hz;

[0097] The micro - displacement actuator adopts an electro - hydraulic hybrid drive method, with an execution accuracy of 0.01 mm, an adjustment stroke of ±15 mm, and a response time of less than 0.5 s;

[0098] The intelligent node is built - in with a 64 - bit microprocessor and 4 GB of flash memory, capable of storing 72 - hour high - frequency sampling data and completing Fourier transform and wavelet analysis locally;

[0099] The wireless data transmission module adopts multi - band communication technology, with working frequency bands covering 433 MHz, 868 MHz, and 2.4 GHz, a data transmission rate of up to 5 Mbps, and an anti - interference ability of not less than 85 dB;

[0100] The intelligent node adopts a tiered power supply system, including mains power, backup batteries, and energy harvesting units, ensuring that the core functions can continue to work for 96 hours in the event of a complete power outage.

[0101] Furthermore, the field control layer adopts a mesh topology structure. The failure of any node does not affect the data transmission between adjacent nodes, and the communication redundancy between nodes is not less than 3.

[0102] The regional control layer adopts a hierarchical redundancy design. Two sets of control units, the main and backup, are configured for each region. When the parameter deviation detected by the main control unit exceeds 2% or the response delay exceeds 300 ms, it automatically switches to the backup unit.

[0103] The central control layer adopts a distributed computing architecture with dynamic load balancing for computing tasks, and the peak processing capacity reaches 5000 complex decision-making analyses per second.

[0104] A three-level data caching mechanism is set up to ensure that the field layer can work independently for 8 hours and the regional layer can work independently for 24 hours in the event of a communication interruption.

[0105] The control network achieves millisecond-level synchronization with a time synchronization accuracy better than 10 μs, ensuring the precise and effective collaborative decision-making of distributed nodes.

[0106] Furthermore, Step 2 includes the following steps:

[0107] Establish a BIM model of the support system and import it into finite element analysis software to construct a mechanical model that comprehensively considers contact nonlinearity, material nonlinearity, and large geometric deformations. At the same time, through multi-condition load analysis, identify stress concentration areas, deformation-sensitive areas, and potential instability positions.

[0108] Based on the finite element analysis results, divide the entire support system into several load control zones. Each control zone is configured with independent control parameters and monitoring schemes, while ensuring the coordinated transition between adjacent zones and the integrity of the load transfer path.

[0109] Set different control parameters for different load control zones.

[0110] Based on the principles of rheology and the early strength development law of concrete, optimize the pouring process parameters, determine the optimal pouring rate curve, and formulate an accurate pouring sequence for different layers and zones.

[0111] Set different monitoring frequencies for different construction stages. Specifically: high-frequency monitoring is adopted in the initial pouring stage, reduced to medium-frequency monitoring during the concrete setting period, and low-frequency monitoring is used during the curing period. At the same time, establish an automatic trigger mechanism based on load mutations to ensure that abnormal situations can be promptly identified and responded to.

[0112] Formulate multi - working - condition emergency plans for possible construction anomalies.

[0113] In summary, by establishing the BIM model of the support system and importing it into the finite - element analysis software, a mechanical model considering various nonlinear factors is constructed, accurately identifying stress - concentration, deformation - sensitive, and potential instability regions. Based on this, the support system is divided into multiple load - control zones, each zone configured with independent control parameters and monitoring schemes to ensure the integrity of the load - transfer path and coordinated transition between zones. At the same time, differential control parameters are set according to the characteristics of different load - control zones, and combined with the rheological mechanics principle and the law of early - strength development of concrete, the pouring process parameters are optimized to determine the optimal pouring - rate curve and the pouring sequence of layers and zones. In addition, differential monitoring frequencies are set for different construction stages, and an automatic trigger mechanism based on load mutations is established to ensure that anomalies can be identified and responded to in a timely manner. Finally, multi - working - condition emergency plans are formulated for possible construction anomalies. This series of measures realizes the refined and intelligent management and control of the support system, effectively improving the safety, reliability, and efficiency of construction, providing scientific guidance and guarantee for the construction of high - formwork projects.

[0114] Furthermore, based on the finite - element analysis results, the entire support system is divided into several load - control zones, each control zone configured with independent control parameters and monitoring schemes, while ensuring coordinated transition between adjacent zones and the integrity of the load - transfer path, including the following steps:

[0115] Based on the finite - element analysis results, the support system is divided into multiple load - control zones according to the stress - distribution characteristics, ensuring that the stress within each zone is uniform and does not exceed a preset threshold, and the division criterion satisfies Ω i ={Ω∣σ(Ω)≤σ 阈值}, where Ω i is the spatial region of the i - th load - control zone; Ω is the spatial region of the entire support system; σ(Ω) is the stress - distribution function within the region Ω\OmegaΩ; σ 阈值 is the stress threshold set during the division of the load - control zone, that is, the standard for judging whether the regional stress is uniform;

[0116] For each load - control zone, configure independent control parameters adapted to its stress characteristics, and adjust the parameters based on P 监测 (i)=f(σ(Ω i ),ε(Ω i ),T(Ω i ),θ(Ω i ))), where P 监测 (i) is the monitoring parameter at the load - control zone i, determining the monitoring scheme for this zone; f is a multi - parameter function, indicating the monitoring parameter P 监测(i) Dependent variable relationship; σ(Ω i ) is the stress distribution within the load control area Ω i ; ε(Ω i ) is the strain distribution within the load control area Ω i ; T(Ω i ) is the temperature field distribution within the load control area Ω i ; θ(Ω i ) is the structural dip angle or deformation angle within the load control area Ω i ;

[0117] By calculating the load balance relationship between adjacent areas, use F 传递 (i,j) = α·(F 区 (i) - F 区 (j)) to ensure the uniform transfer of loads step by step and prevent stress concentration or load mutation. Among them, F 传递 (i,j) is the load transferred between the control area i and the adjacent control area j; α is the load transfer ratio factor, which represents the adjustment coefficient of the load distribution between adjacent areas; F 区 (i) is the total load within the load control area i; F 区 (j) is the total load within the load control area j;

[0118] Introduce a smooth transition mechanism at the junction of adjacent control areas and use to ensure the continuous change of the load gradient and reduce the impact of local stress mutation on the structure; σ 过渡 (i,j) represents the stress transition value between the load control area i and the adjacent control area j; σ(i) and σ(j) are the stresses within the areas i and j respectively; d(i,j) is the distance between the areas i and j; L overlap is the length of the transition area between areas, which controls the smoothness of load transfer;

[0119] Combine the real-time monitoring data with the finite element calculation results, and dynamically adjust the control parameters and load distribution strategy to ensure the stability of the entire support system and the integrity of the load transfer path.

[0120] Furthermore, the differentiated control parameters include:

[0121] The maximum allowable load value in the normal area is 85% of the design load, 75% in the stress concentration area, and 70% in the deformation sensitive area;

[0122] The limit value of the load growth rate: 0.8 kN / min in the normal area, 0.5 kN / min in the core area, and 0.3 kN / min in the special area;

[0123] Deformation warning threshold: L / 1000 for the conventional area, L / 1500 for the core area, and L / 2000 for the special area, where L is the calculated span of the corresponding structure;

[0124] Regulation response sensitivity: 0.8 for the conventional area, 1.2 for the core area, and 1.5 for the special area. The sensitivity value directly affects the system response speed and adjustment intensity;

[0125] Monitoring frequency: 0.5 - 2 Hz during the initial pouring period (0 - 4 h), 0.1 - 0.5 Hz during the setting period (4 - 12 h), 0.02 - 0.05 Hz during the curing period (after 12 h), and automatically increased to 5 Hz in case of anomalies.

[0126] Furthermore, the multi - condition emergency plan includes:

[0127] Single - point overload emergency plan: When the load at a certain point exceeds 90% of the design value, activate 8 - 12 surrounding intelligent nodes for coordinated adjustment to form an "unloading funnel" and disperse the overloaded point load to the surrounding nodes;

[0128] Regional instability emergency plan: When the displacement rate of more than 3 consecutive nodes exceeds 0.8 mm / min for more than 2 minutes, automatically start the regional reinforcement program and simultaneously reduce the support stiffness of the surrounding area by 30 - 50%;

[0129] Sensor failure emergency plan: When abnormal or failed sensor data is detected, perform interpolation estimation based on the data of 15 - 20 adjacent normal nodes, and simultaneously increase the monitoring frequency of the surrounding nodes to 3 times the original;

[0130] Network interruption emergency plan: Adopt a three - level degradation operation strategy. When the network is interrupted, automatically switch to the local control mode, only retain the core security functions, and record the status at 15 - minute intervals;

[0131] Each emergency plan is equipped with a detailed operation flow chart and a resource requirement list, clearly stipulating personnel division of labor, material allocation, and time - node requirements.

[0132] Furthermore, step 3 includes the following steps:

[0133] Compare and analyze the load data real - time monitored by the intelligent nodes with the preset safety threshold. When the load growth rate exceeds 0.5 kN / min or the load difference between adjacent nodes is greater than 15%, automatically generate and execute the pouring parameter adjustment instruction to achieve precise control of the pouring rate, truck arrangement, and working intensity of the transfer pump;

[0134] Based on the load distribution cloud map, calculate and optimize the pouring path in real - time to ensure that the concrete pouring sequence meets the stress requirements and avoid local overload;

[0135] Real-time calculation of the best pouring strategy, optimizing the vibration density and surface flatness of concrete while ensuring that the stress level of the support system does not exceed 75% of the design value, to achieve coordinated control of safety and quality;

[0136] When it is detected that the load in a local area increases too fast or the deformation exceeds expectations, the three-level response is automatically triggered, among which: the first-level response adjusts the pouring rate to 70% of the normal value; the second-level response suspends the pouring in the area and transfers it to the low-load area; the third-level response starts the emergency unloading procedure to ensure that the abnormal load is controlled in a timely and effective manner;

[0137] The load level and change trend of each area are displayed in real time through color gradients. At the same time, a virtual pouring simulation module is established to conduct digital previews before actual pouring, simulate the structural response under different pouring schemes, and optimize the construction plan;

[0138] When an abnormal increase in load is detected in a local area, nearby intelligent nodes work together to evenly distribute the load to the surrounding supporting structures by slightly lifting or unloading to adjust the force transmission path, ensuring that the structural deformation remains continuous and controllable.

[0139] In summary, the intelligent nodes monitor the load data in real time and compare and analyze it with the safety threshold, thus realizing the refined and intelligent control of the concrete pouring process. When the load growth rate or the load difference between adjacent nodes exceeds the set range, the adjustment instructions are automatically generated to accurately control the pouring rate, tanker layout and working intensity of the delivery pump to avoid local overload. The pouring path is optimized based on the load distribution cloud map to ensure that the pouring sequence meets the force requirements. At the same time, the vibration density and surface flatness are optimized under the premise that the stress level does not exceed the design value to achieve the coordinated control of safety and quality. In addition, when the local load grows too fast or the deformation is abnormal, the three-level response mechanism is automatically triggered to adjust the pouring rate, suspend pouring or start the emergency unloading procedure to ensure that the abnormal load is controlled in a timely and effective manner. The load level and change trend are displayed in real time through the color gradient, and the virtual pouring simulation module is used for digital preview to optimize the construction plan. The adjacent intelligent nodes can also work together to adjust the transmission path of the force by micro-lifting or unloading, and evenly distribute the load to the surrounding support structure to ensure that the structural deformation is continuous and controllable. This series of measures effectively improved the safety, reliability and quality of the construction process and reduced construction risks.

[0140] Furthermore, the pouring parameter adjustment has the following characteristics:

[0141] Real-time calculation of load growth rate. When the load growth rate exceeds 0.5kN / min for 90 seconds or exceeds 300 seconds within 180 seconds, the pouring adjustment program is automatically triggered.

[0142] The pouring rate adjustment is divided into five levels: 100% (normal), 85% (slight adjustment), 70% (moderate adjustment), 50% (severe adjustment), and pause;

[0143] Send control instructions to the concrete pump through the wireless communication module to precisely control the pumping rate, and the response time is less than 3 seconds;

[0144] For large-area pouring, simultaneously control the coordinated operation of 2 - 6 pump trucks to ensure that the pouring speed ratio in different areas is strictly optimized according to the load distribution;

[0145] The pouring adjustment instructions are simultaneously pushed to the mobile terminals of on-site management personnel, along with graphical load distribution status and recommended operation instructions.

[0146] Furthermore, based on the load distribution cloud map, calculate and optimize the pouring path in real time to ensure that the concrete pouring sequence meets the stress requirements, including the following steps:

[0147] According to the formula Calculate the cumulative load of each monitoring point at the current construction stage to quantify the stress state of each part after concrete pouring. Among them, σ r (t s ) represents the cumulative load at the rth position at the construction stage t s moment; represents the initial load at the rth position, that is, the load state before the start of construction; s represents the specific steps of a pouring path; represents the load increment at the rth position caused by pouring the concrete in the p k area in each construction step k;

[0148] Set the allowable maximum load σ allow and ensure that σ r (t s ) is not greater than σ allow in each construction step, so as to ensure that each part is within the safe stress range during the construction process;

[0149] Define the risk function to quantify the local overloading risk and provide a measurement index for path optimization to ensure construction safety. Among them, R(s) represents the risk function at the path step s, which is used to measure the ratio between the load at the rth position at the current construction stage and the allowable load;

[0150] Use the objective function Calculate the optimal pouring sequence in real time, balance the load distribution, and prevent sudden increase in local stress. Among them, P represents the pouring path; μ represents the set of all possible pouring paths; λ represents the balance coefficient, which is used to weigh the relationship between load distribution and the smoothness of the pouring path during the optimization process; M represents the total number of path steps during the pouring process; L(p s ) represents the load value at position p s in path step s; L(p s-1 ) represents the load value at position p s-1 in path step s - 1;

[0151] Feed the optimization result back to the construction site, and dynamically adjust the pouring path according to the real-time load cloud map monitoring data to ensure that the concrete pouring sequence always meets the stress requirements and promptly respond to abnormal situations.

[0152] Furthermore, the local area load anomaly adjustment has the following characteristics:

[0153] When local load anomaly is detected, automatically identify the optimal adjustment path, and the number of intelligent nodes involved is all the nodes within 1.5 - 2.5 times the coverage radius around the anomaly point;

[0154] The precision of the micro - lifting adjustment is 0.05mm, the single - time adjustment does not exceed 0.5mm, the cumulative adjustment does not exceed 5mm, and the adjustment interval is not less than 30 seconds;

[0155] The unloading adjustment adopts a gradient strategy. The adjustment amplitude of the nodes within 0.5m from the anomaly point is 100% of the calculated value, 70% within 0.5 - 1.5m, and 40% within 1.5 - 3.0m;

[0156] After each adjustment, observe the structural response for 20 - 60 seconds, and only make the next adjustment when the deformation rate is less than 0.05mm / min;

[0157] The optimization goal of the force transmission path is to make the load distribution uniformity coefficient after adjustment reach more than 0.92, and at the same time ensure that the load increment at any point does not exceed 15% of the original value.

[0158] Furthermore, step 4 includes the following steps:

[0159] By measuring the changes in the internal temperature and sound velocity of the concrete, accurately calculate its real - time strength. When the strength reaches 30%, 60%, and 75% of the design value respectively, automatically trigger the corresponding support adjustment program;

[0160] Adopt a hierarchical unloading strategy, divide the unloading process into three stages: preliminary adjustment period, gradual unloading period, and stable monitoring period, and implement a progressive control strategy of "trial adjustment - observation - readjustment". When the concrete strength reaches 30% of the design value, first make a micro-adjustment of 1 - 3 mm to the edge supports on the non-critical path. After observing the structural response, then orderly advance to the key area to ensure that the unloading process is controllable and reversible;

[0161] Dynamically adjust the unloading speed according to the structural deformation rate to ensure that the deformation at the key position is controlled within the structural span / 2000, and prevent the concrete from cracking or excessive deflection during the process of gradually bearing its own weight;

[0162] Through the data analysis of intelligent nodes, draw the load transfer curve between the support system and the concrete structure in real time. When the abnormal load transfer rate or local stress mutation is detected, automatically pause the unloading program and maintain the current state until the structural stress distribution is stable again;

[0163] For large-span or high-difficulty structures, implement a differential support adjustment strategy. The stiffness adjustment method is adopted in the core stress-bearing area to make the concrete structure gradually bear the load; the reserved deformation method is adopted in the edge and secondary areas to ensure the structural force balance and deformation coordination;

[0164] Record the unloading time, adjustment amount, and structural response data, generate the unloading progress report and safety assessment in real time, and at the same time associate with the BIM model to form a visual simulation of the unloading process.

[0165] In summary, the real-time strength of concrete is deduced by accurately measuring the changes in internal temperature and sound velocity of concrete, and the support adjustment program is automatically triggered when the strength reaches different thresholds. Adopt a hierarchical unloading strategy, divide the unloading process into three stages: preliminary adjustment period, gradual unloading period, and stable monitoring period, and implement a progressive control strategy of "trial adjustment - observation - readjustment" to ensure the controllability and reversibility of the unloading process. During the unloading process, dynamically adjust the unloading speed according to the structural deformation rate, strictly control the deformation at the key position, and prevent the concrete from cracking or excessive deflection. Through the data analysis of intelligent nodes, draw the load transfer curve in real time, and automatically pause the unloading program when abnormalities are detected to ensure the stability of the structural stress distribution. For large-span or high-difficulty structures, implement a differential support adjustment strategy. The stiffness adjustment method is adopted in the core stress-bearing area, and the reserved deformation method is adopted in the edge and secondary areas to ensure the structural force balance and deformation coordination. At the same time, record the data during the unloading process, generate the unloading progress report and safety assessment in real time, and associate with the BIM model to form a visual simulation of the unloading process. These series of measures effectively guarantee the safety and reliability of the concrete structure during the process of gradually bearing its own weight, and improve the construction efficiency and quality.

[0166] Furthermore, the real-time strength deduction of concrete has the following characteristics:

[0167] Embed intelligent temperature sensors at key structural parts, with a vertical spacing of 100 - 150 mm and a horizontal spacing of 1.0 - 1.5 m;

[0168] Adopt neural network algorithm, comprehensively analyze the internal temperature field distribution, sound velocity change and surface rebound value of concrete, and the deduced strength accuracy reaches ±4%;

[0169] According to factors such as concrete mix ratio, ambient temperature, humidity, etc., establish a personalized strength development model to predict the strength change trend within 24 hours;

[0170] Concrete strength detection frequency: once every 1 hour for 0 - 12 hours, once every 2 hours for 12 - 24 hours, and once every 4 hours for 24 - 72 hours;

[0171] Associate strength data with the BIM model to form an internal strength cloud map of the structure, visually showing the strength development of different regions.

[0172] Furthermore, the staged unloading strategy includes:

[0173] Preliminary adjustment period: Start when the concrete strength reaches 30% of the design value, with a duration of 4 - 8 hours. During this period, conduct a micro - adjustment of 1 - 2 mm on the support system and observe the structural response;

[0174] Gradual unloading period: Start when the concrete strength reaches 60% of the design value, with a duration of 12 - 24 hours. Gradually release the load borne by the support system according to the preset unloading curve;

[0175] Stable monitoring period: Start when 90% of the load transfer is completed, with a duration of not less than 24 hours. Focus on monitoring the structural deformation stability and crack development;

[0176] The unloading rate of non - critical path supports is 0.8 - 1.2 kN / h, and the unloading rate of critical path supports is 0.3 - 0.6 kN / h;

[0177] Each adjustment amplitude is controlled within 25 - 30% of the calculated value, and the adjustment interval is 30 - 90 minutes. During this period, closely monitor the structural response.

[0178] Furthermore, the load transfer curve has the following characteristics:

[0179] Draw the load transfer curve every 5 minutes, and record the change in the load sharing ratio between the support system and the concrete structure;

[0180] The normal load transfer curve is S-shaped, with a transfer rate of 1-2% / h in the initial stage (0-30%), 3-5% / h in the middle stage (30-70%), and 1-3% / h in the later stage (70-100%);

[0181] When the slope mutation of the load transfer curve is detected (the change rate exceeds 200%) or a plateau appears (the growth rate is less than 0.1% for 60 minutes), the system automatically pauses unloading and issues an alarm;

[0182] During the load transfer process, the local stress distribution is monitored in real time. When the stress concentration coefficient exceeds 1.5, the unloading sequence is automatically adjusted;

[0183] Save the historical load transfer curve, compare and analyze it with the current curve, and predict potential risk points.

[0184] Furthermore, the differential support adjustment strategy has the following characteristics:

[0185] The stiffness adjustment method is adopted in the core stress area. The support stiffness is gradually reduced in 20-30 steps through hydraulic devices, and the stiffness is reduced by 3-5% in each step;

[0186] The reserved deformation method is adopted in the edge and secondary areas. The initial reserved deformation amount is 80-90% of the theoretical calculated value, and then it is dynamically adjusted according to the actual monitoring data;

[0187] For large-span structures with a span > 18m, a synchronous symmetric unloading strategy is adopted to ensure that the difference in the unloading progress on both sides is controlled within 5%;

[0188] For transfer layer structures, a layered unloading strategy is adopted, following the order of "lower-middle-upper", and the unloading ratio is "3-4-3";

[0189] For special-shaped structures, a topology optimization algorithm is adopted to calculate the optimal unloading path to ensure a smooth transition of the stress streamline.

[0190] Furthermore, step 5 includes the following steps:

[0191] When local load fluctuations are detected, the built-in buffer device of the intelligent node automatically absorbs the stress peak value, preventing the impact load from being directly transmitted to the overall support structure, and at the same time recording the deformation amount and recovery time;

[0192] When the load reaches 85% of the design value, it enters the yellow warning state, and the monitoring frequency is automatically increased; when it reaches 95% of the design value, the orange warning is activated and the adjacent nodes are triggered to share the load; when it exceeds the design value, the red warning is immediately executed, and the active unloading program and the structure reinforcement strategy are started;

[0193] Dynamically calculate the optimal load transfer path based on the topology optimization algorithm. By fine-tuning the support stiffness of adjacent nodes, an elastic support network with "soft-hard" alternation is formed, effectively smoothing the stress gradient in the overload area and evenly dispersing the load to the surrounding low-load areas to ensure the overall structural force balance.

[0194] When the structural deformation trend intensifies, automatically calculate the best reinforcement position and support angle, and quickly complete the support deployment through remote control or the nearby robotic arm to improve the bearing capacity of the local area.

[0195] Automatically generate event tags and classify them according to the severity level. At the same time, provide a preliminary diagnosis of the cause of the failure and disposal suggestions to guide on-site personnel to implement precise intervention and improve the response efficiency.

[0196] By comparing the historical load fluctuation patterns and intervention effects, continuously optimize the warning threshold and response strategy.

[0197] In summary, the built-in buffer device of the intelligent node absorbs the stress peak of local load fluctuations, prevents the impact load from being directly transmitted to the overall support structure, and records the deformation amount and recovery time to provide data support for subsequent analysis. When the load reaches 85%, 95% of the design value and exceeds the design value, yellow, orange and red warnings are triggered respectively. Automatically adjust the monitoring frequency, start the adjacent nodes to share the load cooperatively, and execute the active unloading and structural reinforcement strategies to ensure the structural safety. Dynamically calculate the optimal load transfer path based on the topology optimization algorithm. By fine-tuning the support stiffness of adjacent nodes, an elastic support network with "soft-hard" alternation is formed, effectively smoothing the stress gradient in the overload area and evenly dispersing the load to the surrounding low-load areas to ensure the overall structural force balance. When the structural deformation trend intensifies, automatically calculate the best reinforcement position and support angle, and quickly complete the support deployment through remote control or the robotic arm to improve the bearing capacity of the local area. In addition, automatically generate event tags and classify them, provide a preliminary diagnosis of the cause of the failure and disposal suggestions to guide on-site personnel to implement precise intervention and improve the response efficiency. At the same time, by comparing the historical load fluctuation patterns and intervention effects, continuously optimize the warning threshold and response strategy to further improve the intelligent level and response ability. These series of measures effectively improve the safety, reliability and intelligent management level of the support system for high formwork engineering, and reduce the construction risk.

[0198] Furthermore, the intelligent node buffer device has the following parameters:

[0199] The buffer device adopts a gas-liquid composite structure, with a buffer stroke of 8 - 20 mm and a maximum absorption capacity of 35% of the design load.

[0200] The buffer response time is less than 0.15 seconds, and the maximum deceleration is controlled within 5g, effectively preventing structural damage caused by impact loads.

[0201] The buffer device is equipped with a displacement sensor and an accelerometer, with a sampling frequency of 200 Hz, accurately recording the load-time curve during the impact process;

[0202] The device has a three-stage buffering mechanism: mild impact (<10% of the design load) is absorbed through elastic deformation; moderate impact (10 - 25%) is absorbed through controllable damping; severe impact (>25%) is absorbed through a controllable hydraulic system;

[0203] The buffer device has a self-recovery function, recovering 90% of its function within 8 - 15 seconds after mild and moderate impacts, and 80% of its function within 30 - 60 seconds after severe impact.

[0204] Furthermore, based on the topology optimization algorithm, the optimal load transfer path is dynamically calculated. By fine-tuning the support stiffness of adjacent nodes, an elastic support network with "soft-hard" alternation is formed, effectively flattening the stress gradient in the overload area and evenly dispersing the load to the surrounding low-load areas to ensure the overall structural force balance, including the following steps:

[0205] Construct an initial finite element model and set the load and constraint conditions, establish a discrete grid in the simulation domain and define the initial density and stiffness properties of each node, and at the same time establish the optimization objective function and related constraint conditions;

[0206] Execute the topology optimization iterative calculation, identify the key load transfer paths through sensitivity analysis, dynamically update the density values of each grid element according to the stress distribution, retain the main load-bearing paths and gradually eliminate the inefficient material distribution areas until the load transfer efficiency reaches the optimum;

[0207] Based on the optimization results, construct a "soft-hard" alternating layout plan, arrange high-stiffness support nodes around the high-stress area, and configure relatively flexible support structures in the adjacent areas to form a stiffness gradient distribution to ensure that the load can be smoothly transferred along the preset path;

[0208] Adjust the stiffness coefficients of each node through parametric design to make the stiffness distribution and stress distribution form a complementary relationship, establish an elastic support network that can adaptively deform, and improve the structure's adaptability to local overload;

[0209] Compare the stress concentration coefficient and peak stress level before and after optimization to confirm whether the stress gradient in the overload area is effectively flattened and whether the load is successfully dispersed to the surrounding low-load areas;

[0210] Analyze the performance of the optimized structure under different load conditions and boundary conditions to ensure that it can maintain force balance in various operating environments.

[0211] Furthermore, the structural deformation trend monitoring and reinforcement have the following parameters:

[0212] The second derivative analysis method is used to evaluate the deformation trend. When the deformation acceleration is greater than 0.001 mm / min² for more than 5 minutes continuously, it is determined that the deformation trend intensifies;

[0213] The calculation of the optimal reinforcement position considers 19 key constraint conditions, including the existing support layout, load distribution, deformation curve and material properties;

[0214] The response time of the emergency support deployment: 10 - 15 minutes in the manual intervention mode, 3 - 5 minutes in the robotic arm assisted mode, and less than 90 seconds in the fully automatic mode;

[0215] The optimization range of the emergency support angle is ±15° in the vertical direction, and the position accuracy requirement of the support point is ±50 mm;

[0216] The evaluation index of the reinforcement effect: the deformation rate is reduced to less than 30% of the original within 15 minutes after deployment, and the local bearing capacity is increased by 40 - 60%.

[0217] Furthermore, step 6 includes the following steps:

[0218] Adopt a hierarchical and progressive data acquisition strategy to capture load changes, displacement responses and environmental parameters in real time, and synchronously record the construction progress and operation instructions. Screen key data through edge computing and upload it to the cloud to construct a multi-dimensional construction holographic record with spatio-temporal tagging;

[0219] Adopt the Bayesian optimization method to dynamically adjust the load threshold, response sensitivity and control gain. By comparing the stability and energy consumption efficiency under different parameter combinations, automatically generate the optimal control parameter set to ensure that the support system accurately adapts to different structural types and construction conditions;

[0220] Refine and standardize the construction experience and abnormal handling strategies in individual projects, and establish a retrievable construction knowledge graph through semantic annotation and correlation mapping to improve the knowledge reuse rate and the intelligent level of project management;

[0221] Compare the actual construction process with the virtual simulation model in real time, quantify the difference between the predicted deviation and the actual state, and continuously optimize based on the closed-loop feedback mechanism to ensure construction accuracy and structural safety;

[0222] By comprehensively analyzing the spatio-temporal distribution law and correlation factors of historical abnormal data, accurately identify potential risk points and weak links, deploy monitoring resources and prevention and control measures in advance, transform passive emergency into active prevention, and significantly reduce the probability of construction risks.

[0223] In summary, the data acquisition strategy of hierarchical progression captures multi-dimensional data such as load changes, displacement responses, and environmental parameters in real time, and synchronously records the construction progress and operation instructions. Edge computing is used to screen key data and upload it to the cloud, constructing a holographic record of construction with spatio-temporal tagging. The Bayesian optimization method is used to dynamically adjust the load threshold, response sensitivity, and control gain. By comparing the stability and energy consumption efficiency under different parameter combinations, an optimal control parameter set is automatically generated to ensure that the support system accurately adapts to different structural types and construction conditions. At the same time, the construction experience and abnormal handling strategies of individual projects are refined and standardized, and a retrievable construction knowledge graph is established to improve the knowledge reuse rate and the intelligent level of project management. In addition, the actual construction process is compared with the virtual simulation model in real time, the prediction deviation and the actual state difference are quantified, and continuous optimization is carried out based on the closed-loop feedback mechanism to ensure construction accuracy and structural safety. Through the comprehensive analysis of the spatio-temporal distribution law and correlation factors of historical abnormal data, potential risk points and weak links are accurately identified, monitoring resources and prevention and control measures are deployed in advance, and passive emergency response is transformed into active prevention, significantly reducing the probability of construction risks. This series of measures realizes the refined management, intelligent decision-making, and active risk prevention of the construction process, significantly improving the construction efficiency, quality, and safety.

[0224] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or equivalently replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A construction method for a high formwork engineering support system, characterized in that, It includes the following steps: Step 1: Arrange intelligent nodes with load perception and automatic adjustment functions in the high formwork support system, construct a hierarchical control network, and achieve information sharing and group collaborative control among the nodes; Step 2: Divide the support system into multiple interrelated load control areas, set differentiated load control parameters, and optimize the concrete pouring rate, support adjustment strategy, and monitoring frequency; Step 3: Dynamically adjust the concrete pouring rate, sequence, and position according to the real-time monitoring data, and automatically optimize the pouring path; Step 4: When the concrete strength reaches the preset threshold, automatically adjust the support stiffness or reserved deformation amount according to the preset program, so that the concrete structure gradually bears its own weight; Step 5: Automatically compensate for the temporary load. When the load exceeds the safety threshold, trigger the load redistribution mechanism and reinforcement device, and at the same time start the alarm and push the abnormal data; Step 6: Continuously optimize the control parameters, accumulate construction data, construct a digital construction record and knowledge base, and realize the intelligent iterative upgrade of the construction method.

2. The construction method of a high formwork engineering support system according to claim 1, characterized in that, Step 1 includes the following steps: According to the structural characteristics and force analysis of the support system, determine the positions of the key control points, and install intelligent nodes at the intersections of the vertical poles, the connections of the horizontal bars, and the load concentration areas; Configure a micro-displacement actuator for each intelligent node to achieve active deformation control of the support structure; Integrate a wireless data transmission module into each intelligent node; Establish a three-level control network architecture, where the on-site control layer consists of intelligent nodes distributed in various regions of the support system, the regional control layer is responsible for coordinating the operation of the nodes in the same load area, and the central control layer coordinates the overall operation strategy and provides data analysis and decision-making support; When an individual node detects an abnormal load change, the adjacent nodes will automatically adjust the support stiffness according to the preset logic, so as to achieve a distributed adaptive control mechanism; Verify the measurement accuracy of each node by applying known loads, and simulate various working conditions to test the collaborative response ability between the nodes, ensuring that the entire control network is in the best working state before formal construction.

3. The construction method of a high formwork engineering support system according to claim 2, characterized in that Each intelligent node is equipped with a high-precision pressure sensor with a measurement accuracy of ±0.01 kN, a range of 0 - 150 kN, and the sampling frequency can be adaptively adjusted within the range of 0.2 - 20 Hz; the micro-displacement actuator adopts an electro-hydraulic hybrid drive method, with an execution accuracy of 0.01 mm, an adjustment stroke of ±15 mm, and a response time of less than 0.5 s; the intelligent node is built-in with a 64-bit microprocessor and 4 GB of flash memory, which can store 72 hours of high-frequency sampling data and complete Fourier transform and wavelet analysis locally; the wireless data transmission module adopts multi-band communication technology, with a working frequency band covering 433 MHz, 868 MHz, and 2.4 GHz, a data transmission rate of 5 Mbps, and an anti-interference ability of not less than 85 dB; the intelligent node adopts a hierarchical power supply system, including mains electricity, backup batteries, and energy harvesting units, ensuring that the core functions can continue to work for 96 hours in the event of a complete power outage.

4. The construction method of a high formwork engineering support system according to claim 1, characterized in that, Step 2 includes the following steps: Establish a BIM model of the support system and import it into the finite element analysis software to construct a mechanical model that comprehensively considers contact nonlinearity, material nonlinearity and large geometric deformation. At the same time, through multi-condition load analysis, identify stress concentration areas, deformation sensitive areas and potential unstable locations; Based on the results of finite element analysis, the entire support system is divided into several load control zones, each of which is configured with independent control parameters and monitoring schemes, while ensuring the coordinated transition between adjacent zones and the integrity of the load transfer path; Set differentiated control parameters for different load control areas; Based on the principles of rheology and the early strength development law of concrete, the pouring process parameters are optimized, the optimal pouring rate curve is determined, and an accurate layered and zoned pouring sequence is formulated; Different monitoring frequencies are set for different construction stages, including: high-frequency monitoring is used in the initial pouring period, medium-frequency monitoring is reduced to monitoring during the concrete setting period, and low-frequency monitoring is used during the curing period; at the same time, an automatic trigger mechanism based on load mutation is established to ensure that abnormal situations can be identified and responded to in a timely manner; In response to possible abnormal construction situations, formulate multi-condition emergency plans.

5. A construction method for a high formwork engineering support system according to claim 1, characterized in that, Step 3 includes the following steps: The load data monitored in real time by the intelligent node is compared and analyzed with the preset safety threshold. When the load growth rate exceeds 0.5kN / min or the load difference between adjacent nodes is greater than 15%, the pouring parameter adjustment instruction is automatically generated and executed to achieve precise control of the pouring rate, tank truck layout and delivery pump working intensity. Based on the load distribution cloud map, the pouring path is calculated and optimized in real time to ensure that the concrete pouring sequence meets the force requirements and avoid local overload; Real-time calculation of the best pouring strategy, optimizing the vibration density and surface flatness of concrete while ensuring that the stress level of the support system does not exceed 75% of the design value, to achieve coordinated control of safety and quality; When it is detected that the load in a local area increases too fast or the deformation exceeds expectations, the three-level response is automatically triggered, among which: the first-level response adjusts the pouring rate to 70% of the normal value; the second-level response suspends the pouring in the area and transfers it to the low-load area; the third-level response starts the emergency unloading procedure to ensure that the abnormal load is controlled in a timely and effective manner; The load level and change trend of each area are displayed in real time through color gradients. At the same time, a virtual pouring simulation module is established to conduct digital previews before actual pouring, simulate the structural response under different pouring schemes, and optimize the construction plan; When an abnormal increase in load is detected in a local area, nearby intelligent nodes work together to evenly distribute the load to the surrounding supporting structures by slightly lifting or unloading to adjust the force transmission path, ensuring that the structural deformation remains continuous and controllable.

6. A construction method for a high formwork engineering support system according to claim 1, characterized in that, Step 4 includes the following steps: By measuring the changes in temperature and sound velocity inside the concrete, its real-time strength can be accurately calculated. When the strength reaches 30%, 60% and 75% of the design value, the corresponding support adjustment program is automatically triggered; A hierarchical unloading strategy is adopted, and the unloading process is divided into three stages: preparatory adjustment period, gradual unloading period and stable monitoring period. A progressive control strategy of "trial adjustment-observation-re-adjustment" is implemented. When the concrete strength reaches 30% of the design value, a micro-adjustment of 1-3mm is first made to the edge support of the non-critical path. After observing the structural response, it is then orderly advanced to the critical area to ensure that the unloading process is controllable and reversible. Dynamically adjust the unloading speed according to the structural deformation rate to ensure that the deformation of key positions is controlled within the structural span / 2000, preventing the concrete from cracking or excessive deflection in the process of gradually bearing its own weight; Through data analysis of intelligent nodes, the load transfer curve between the support system and the concrete structure is drawn in real time. When an abnormal load transfer rate or a sudden change in local stress is detected, the unloading program is automatically suspended and the current state is maintained until the structural stress distribution stabilizes again. For large-span or high-difficulty structures, differentiated support adjustment strategies are implemented. The core stress-bearing area adopts the stiffness adjustment method to gradually make the concrete structure bear the load by reducing the support stiffness; the edge and secondary areas adopt the reserved deformation method to ensure the structural force balance and deformation coordination; Record the unloading time, adjustment amount and structural response data, generate unloading progress report and safety assessment in real time, and associate with the BIM model to form a visual unloading process simulation.

7. A construction method for a high formwork engineering support system according to claim 1, characterized in that, Step 5 includes the following steps: When local load fluctuations are detected, the built-in buffer device of the intelligent node automatically absorbs the stress peak to prevent the impact load from being directly transmitted to the overall support structure, while recording the deformation and recovery time; When the load reaches 85% of the design value, it enters the yellow warning state and automatically increases the monitoring frequency; when it reaches 95% of the design value, it starts the orange warning and triggers the collaborative sharing of adjacent nodes; when it exceeds the design value, it immediately executes the red warning and starts the active unloading program and structural reinforcement strategy; The optimal load transfer path is dynamically calculated based on the topology optimization algorithm. By fine-tuning the support stiffness of adjacent nodes, a "soft-hard" alternating elastic support network is formed to effectively smooth the stress gradient in the overloaded area and evenly distribute the load to the surrounding low-load areas to ensure balanced stress on the overall structure. When the structural deformation trend is detected to be increasing, the optimal reinforcement position and support angle are automatically calculated, and the support deployment is quickly completed through remote control or the nearest robotic arm to improve the bearing capacity of the local area; Automatically generate event labels and classify them according to severity, while providing preliminary diagnosis of fault causes and disposal suggestions, guiding on-site personnel to implement precise intervention and improve response efficiency; By comparing historical load fluctuation patterns and intervention effects, the warning thresholds and response strategies are continuously optimized.

8. A construction method for a high formwork engineering support system according to claim 7, characterized in that, The intelligent node buffer device has the following parameters: The buffer device adopts a gas-liquid composite structure, with a buffer stroke of 8-20mm and a maximum suction capacity of 35% of the design load; The buffer response time is less than 0.15 seconds, and the maximum deceleration is controlled within 5g, effectively preventing structural damage caused by impact loads; The buffer device has built-in displacement sensor and accelerometer, with a sampling frequency of 200Hz, which can accurately record the load-time curve during the impact process; The device is equipped with a three - level buffering mechanism: minor impacts are absorbed through elastic deformation; moderate impacts are absorbed through controllable damping; severe impacts are absorbed through a controllable hydraulic system; The buffering device has a self - recovery function. It can recover 90% of its function within 8 - 15 seconds after minor and moderate impacts, and 80% of its function within 30 - 60 seconds after severe impacts.

9. A construction method for a high formwork engineering support system according to claim 7, characterized in that, Based on the topology optimization algorithm, dynamically calculate the optimal load transfer path. By fine - tuning the support stiffness of adjacent nodes, form an elastic support network with "soft - hard" alternation, effectively smooth the stress gradient in the overload area, and evenly disperse the load to the surrounding low - load areas to ensure the overall structure is evenly stressed. The steps include: Construct an initial finite - element model and set the load and constraint conditions. Establish discrete grids in the simulation domain and define the initial density and stiffness properties of each node. At the same time, establish the optimization objective function and related constraint conditions; Execute the topology optimization iterative calculation. Identify the key load transfer paths through sensitivity analysis, dynamically update the density values of each grid element according to the stress distribution, retain the main load - bearing paths and gradually eliminate the inefficient material distribution areas until the load transfer efficiency reaches the optimum; Based on the optimization results, construct a "soft - hard" alternating layout plan. Arrange high - stiffness support nodes around the high - stress areas, and configure relatively flexible support structures in the adjacent areas to form a stiffness gradient distribution to ensure that the load can be smoothly transferred along the preset path; Adjust the stiffness coefficients of each node through parametric design, make the stiffness distribution and stress distribution form a complementary relationship, establish an elastic support network that can adaptively deform, and improve the adaptability of the structure to local overloads; Compare the stress concentration coefficient and peak stress level before and after optimization to confirm whether the stress gradient in the overload area is effectively smoothed and whether the load is successfully dispersed to the surrounding low - load areas; Analyze the performance of the optimized structure under different load conditions and boundary conditions to ensure that it can maintain even stress under various operating environments.

10. A construction method for a high formwork engineering support system according to claim 1, characterized in that, Step 6 includes the following steps: Adopt a hierarchical and progressive data - acquisition strategy to capture load changes, displacement responses, and environmental parameters in real - time, and synchronously record the construction progress and operation instructions. Screen key data through edge computing and upload it to the cloud to construct a multi - dimensional construction holographic record with spatio - temporal tagging; Adopt the Bayesian optimization method to dynamically adjust the load threshold, response sensitivity, and control gain. By comparing the stability and energy - consumption efficiency under different parameter combinations, automatically generate an optimal control parameter set to ensure that the support system can accurately adapt to different structural types and construction conditions; Refine and standardize the construction experience and abnormal handling strategies in a single project, and establish a retrievable construction knowledge graph through semantic annotation and association mapping to improve the knowledge reuse rate and the intelligent level of project management; Compare the actual construction process with the virtual simulation model in real - time, quantify the prediction deviation and the actual state difference, and continuously optimize based on the closed - loop feedback mechanism to ensure construction accuracy and structural safety; By comprehensively analyzing the spatio-temporal distribution patterns and associated factors of historical abnormal data, accurately identifying potential risk points and weak links, and deploying monitoring resources and prevention and control measures in advance, the passive emergency response is transformed into active prevention, significantly reducing the probability of construction risks.

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